{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "scratchpad",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sayakpaul/Training-BatchNorm-and-Only-BatchNorm/blob/master/CIFAR_Subset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "lIYdn1woOS1n",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "77161273-ca85-41f3-bc3f-88701ab01a4f"
      },
      "source": [
        "# TensorFlow Imports\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.2.0-rc3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Z8L9GgmRFd_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "8219242c-b87e-471b-8172-b24b522ea862"
      },
      "source": [
        "# Which GPU?\n",
        "!nvidia-smi"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sun May  3 07:03:45 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 440.64.00    Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   36C    P0    27W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                       GPU Memory |\n",
            "|  GPU       PID   Type   Process name                             Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5-_4ROoYRHJq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!wget https://raw.githubusercontent.com/GoogleCloudPlatform/keras-idiomatic-programmer/master/zoo/resnet/resnet_cifar10.py"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "txX7OoEpROgI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Other imports\n",
        "from tensorflow.keras.layers import *\n",
        "from tensorflow.keras.models import *\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "import resnet_cifar10\n",
        "import numpy as np\n",
        "import time\n",
        "\n",
        "# Random seed fixation\n",
        "tf.random.set_seed(666)\n",
        "np.random.seed(666)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1NlIHQ-rRWlX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_training_model():\n",
        "    # ResNet20\n",
        "    n = 2\n",
        "    depth =  n * 9 + 2\n",
        "    n_blocks = ((depth - 2) // 9) - 1\n",
        "\n",
        "    # The input tensor\n",
        "    inputs = Input(shape=(32, 32, 3))\n",
        "\n",
        "    # The Stem Convolution Group\n",
        "    x = resnet_cifar10.stem(inputs)\n",
        "\n",
        "    # The learner\n",
        "    x = resnet_cifar10.learner(x, n_blocks)\n",
        "\n",
        "    # The Classifier for 10 classes\n",
        "    outputs = resnet_cifar10.classifier(x, 10)\n",
        "\n",
        "    # Instantiate the Model\n",
        "    model = Model(inputs, outputs)\n",
        "    \n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sjq25fUmRY4e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_training(H):\n",
        "\t# construct a plot that plots and saves the training history\n",
        "\twith plt.xkcd():\n",
        "\t\tplt.figure()\n",
        "\t\tplt.plot(H.history[\"loss\"], label=\"train_loss\")\n",
        "\t\tplt.plot(H.history[\"val_loss\"], label=\"val_loss\")\n",
        "\t\tplt.plot(H.history[\"accuracy\"], label=\"train_acc\")\n",
        "\t\tplt.plot(H.history[\"val_accuracy\"], label=\"val_acc\")\n",
        "\t\tplt.title(\"Training Loss and Accuracy\")\n",
        "\t\tplt.xlabel(\"Epoch #\")\n",
        "\t\tplt.ylabel(\"Loss/Accuracy\")\n",
        "\t\tplt.legend(loc=\"lower left\")\n",
        "\t\tplt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jp-VUHKiRqo4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Load the training set of CIFAR10\n",
        "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DIQPxZgkRzMl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Train and validation on first 10000 and 2000 images respectively\n",
        "(x_train_frac, y_train_frac) = x_train[:10000], y_train[:10000]\n",
        "(x_test_frac, y_test_frac) = x_test[:2000], y_test[:2000]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dNhaSmKjR2Vq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "BATCH_SIZE = 128\n",
        "\n",
        "def normalize(image, label):\n",
        "    return tf.image.convert_image_dtype(image, tf.float32), label\n",
        "\n",
        "def augment(image,label):\n",
        "    image = tf.image.resize_with_crop_or_pad(image, 40, 40) # Add 8 pixels of padding\n",
        "    image = tf.image.random_crop(image, size=[32, 32, 3]) # Random crop back to 28x28\n",
        "    image = tf.image.random_brightness(image, max_delta=0.5) # Random brightness\n",
        "    image = tf.clip_by_value(image, 0., 1.)\n",
        "    \n",
        "    return image, label\n",
        "\n",
        "train_ds = tf.data.Dataset.from_tensor_slices((x_train_frac, y_train_frac))\n",
        "train_ds = (\n",
        "    train_ds\n",
        "    .shuffle(1024)\n",
        "    .map(normalize, num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
        "    .map(augment, num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
        "    .batch(BATCH_SIZE)\n",
        "    .prefetch(tf.data.experimental.AUTOTUNE)\n",
        ")\n",
        "\n",
        "test_ds = tf.data.Dataset.from_tensor_slices((x_test_frac, y_test_frac))\n",
        "test_ds = (\n",
        "    test_ds\n",
        "    .map(normalize, num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
        "    .batch(BATCH_SIZE)\n",
        "    .prefetch(tf.data.experimental.AUTOTUNE)\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ymnw4EirV8HT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "13aaac6d-3d26-4af2-e0d1-6cb836db49b4"
      },
      "source": [
        "model = get_training_model()\n",
        "model.summary()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_4\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_5 (InputLayer)            [(None, 32, 32, 3)]  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_232 (Conv2D)             (None, 32, 32, 16)   448         input_5[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_224 (BatchN (None, 32, 32, 16)   64          conv2d_232[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_224 (ReLU)                (None, 32, 32, 16)   0           batch_normalization_224[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_234 (Conv2D)             (None, 32, 32, 16)   272         re_lu_224[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_225 (BatchN (None, 32, 32, 16)   64          conv2d_234[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_225 (ReLU)                (None, 32, 32, 16)   0           batch_normalization_225[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_235 (Conv2D)             (None, 32, 32, 16)   2320        re_lu_225[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_226 (BatchN (None, 32, 32, 16)   64          conv2d_235[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_226 (ReLU)                (None, 32, 32, 16)   0           batch_normalization_226[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_236 (Conv2D)             (None, 32, 32, 64)   1088        re_lu_226[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_233 (Conv2D)             (None, 32, 32, 64)   1088        re_lu_224[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_227 (BatchN (None, 32, 32, 64)   256         conv2d_236[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "add_72 (Add)                    (None, 32, 32, 64)   0           conv2d_233[0][0]                 \n",
            "                                                                 batch_normalization_227[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_227 (ReLU)                (None, 32, 32, 64)   0           add_72[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_237 (Conv2D)             (None, 32, 32, 16)   1040        re_lu_227[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_228 (BatchN (None, 32, 32, 16)   64          conv2d_237[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_228 (ReLU)                (None, 32, 32, 16)   0           batch_normalization_228[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_238 (Conv2D)             (None, 32, 32, 16)   2320        re_lu_228[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_229 (BatchN (None, 32, 32, 16)   64          conv2d_238[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_229 (ReLU)                (None, 32, 32, 16)   0           batch_normalization_229[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_239 (Conv2D)             (None, 32, 32, 64)   1088        re_lu_229[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_230 (BatchN (None, 32, 32, 64)   256         conv2d_239[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "add_73 (Add)                    (None, 32, 32, 64)   0           batch_normalization_230[0][0]    \n",
            "                                                                 re_lu_227[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_230 (ReLU)                (None, 32, 32, 64)   0           add_73[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_241 (Conv2D)             (None, 32, 32, 64)   4160        re_lu_230[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_231 (BatchN (None, 32, 32, 64)   256         conv2d_241[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_231 (ReLU)                (None, 32, 32, 64)   0           batch_normalization_231[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_242 (Conv2D)             (None, 16, 16, 64)   36928       re_lu_231[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_232 (BatchN (None, 16, 16, 64)   256         conv2d_242[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_232 (ReLU)                (None, 16, 16, 64)   0           batch_normalization_232[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_243 (Conv2D)             (None, 16, 16, 128)  8320        re_lu_232[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_240 (Conv2D)             (None, 16, 16, 128)  8320        re_lu_230[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_233 (BatchN (None, 16, 16, 128)  512         conv2d_243[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "add_74 (Add)                    (None, 16, 16, 128)  0           conv2d_240[0][0]                 \n",
            "                                                                 batch_normalization_233[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_233 (ReLU)                (None, 16, 16, 128)  0           add_74[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_244 (Conv2D)             (None, 16, 16, 64)   8256        re_lu_233[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_234 (BatchN (None, 16, 16, 64)   256         conv2d_244[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_234 (ReLU)                (None, 16, 16, 64)   0           batch_normalization_234[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_245 (Conv2D)             (None, 16, 16, 64)   36928       re_lu_234[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_235 (BatchN (None, 16, 16, 64)   256         conv2d_245[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_235 (ReLU)                (None, 16, 16, 64)   0           batch_normalization_235[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_246 (Conv2D)             (None, 16, 16, 128)  8320        re_lu_235[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_236 (BatchN (None, 16, 16, 128)  512         conv2d_246[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "add_75 (Add)                    (None, 16, 16, 128)  0           batch_normalization_236[0][0]    \n",
            "                                                                 re_lu_233[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_236 (ReLU)                (None, 16, 16, 128)  0           add_75[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_248 (Conv2D)             (None, 16, 16, 128)  16512       re_lu_236[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_237 (BatchN (None, 16, 16, 128)  512         conv2d_248[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_237 (ReLU)                (None, 16, 16, 128)  0           batch_normalization_237[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_249 (Conv2D)             (None, 8, 8, 128)    147584      re_lu_237[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_238 (BatchN (None, 8, 8, 128)    512         conv2d_249[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_238 (ReLU)                (None, 8, 8, 128)    0           batch_normalization_238[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_250 (Conv2D)             (None, 8, 8, 256)    33024       re_lu_238[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_247 (Conv2D)             (None, 8, 8, 256)    33024       re_lu_236[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_239 (BatchN (None, 8, 8, 256)    1024        conv2d_250[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "add_76 (Add)                    (None, 8, 8, 256)    0           conv2d_247[0][0]                 \n",
            "                                                                 batch_normalization_239[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_239 (ReLU)                (None, 8, 8, 256)    0           add_76[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_251 (Conv2D)             (None, 8, 8, 128)    32896       re_lu_239[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_240 (BatchN (None, 8, 8, 128)    512         conv2d_251[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_240 (ReLU)                (None, 8, 8, 128)    0           batch_normalization_240[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_252 (Conv2D)             (None, 8, 8, 128)    147584      re_lu_240[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_241 (BatchN (None, 8, 8, 128)    512         conv2d_252[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_241 (ReLU)                (None, 8, 8, 128)    0           batch_normalization_241[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_253 (Conv2D)             (None, 8, 8, 256)    33024       re_lu_241[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_242 (BatchN (None, 8, 8, 256)    1024        conv2d_253[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "add_77 (Add)                    (None, 8, 8, 256)    0           batch_normalization_242[0][0]    \n",
            "                                                                 re_lu_239[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_242 (ReLU)                (None, 8, 8, 256)    0           add_77[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_243 (BatchN (None, 8, 8, 256)    1024        re_lu_242[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_243 (ReLU)                (None, 8, 8, 256)    0           batch_normalization_243[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_4 (AveragePoo (None, 1, 1, 256)    0           re_lu_243[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "flatten_4 (Flatten)             (None, 256)          0           average_pooling2d_4[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "dense_4 (Dense)                 (None, 10)           2570        flatten_4[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 575,114\n",
            "Trainable params: 571,114\n",
            "Non-trainable params: 4,000\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lNCYm8J2WiMh",
        "colab_type": "text"
      },
      "source": [
        "- Total params: 575,114\n",
        "- Trainable params: 571,114\n",
        "- Non-trainable params: 4,000\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WOv6rFsbR9z2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "57d68ad1-62e3-4ad9-b140-afb7c0a43169"
      },
      "source": [
        "# Train model\n",
        "model = get_training_model()\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"sgd\", metrics=[\"accuracy\"])\n",
        "start = time.time()\n",
        "h = model.fit(train_ds,\n",
        "         validation_data=test_ds,\n",
        "         epochs=75)\n",
        "end = time.time()\n",
        "print(\"Network takes {:.3f} seconds to train\".format(end - start))\n",
        "plot_training(h)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/75\n",
            "79/79 [==============================] - 3s 44ms/step - loss: 2.2924 - accuracy: 0.1321 - val_loss: 2.2533 - val_accuracy: 0.1605\n",
            "Epoch 2/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 2.1768 - accuracy: 0.1951 - val_loss: 2.1988 - val_accuracy: 0.1830\n",
            "Epoch 3/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 2.0928 - accuracy: 0.2296 - val_loss: 2.1401 - val_accuracy: 0.1970\n",
            "Epoch 4/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 2.0396 - accuracy: 0.2411 - val_loss: 2.0690 - val_accuracy: 0.2325\n",
            "Epoch 5/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.9946 - accuracy: 0.2577 - val_loss: 2.0083 - val_accuracy: 0.2705\n",
            "Epoch 6/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.9692 - accuracy: 0.2634 - val_loss: 1.9671 - val_accuracy: 0.2740\n",
            "Epoch 7/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.9359 - accuracy: 0.2877 - val_loss: 1.9281 - val_accuracy: 0.2995\n",
            "Epoch 8/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.9137 - accuracy: 0.2797 - val_loss: 2.0185 - val_accuracy: 0.2510\n",
            "Epoch 9/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.8994 - accuracy: 0.2911 - val_loss: 1.8850 - val_accuracy: 0.2965\n",
            "Epoch 10/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.8756 - accuracy: 0.3011 - val_loss: 1.8778 - val_accuracy: 0.2995\n",
            "Epoch 11/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.8613 - accuracy: 0.2959 - val_loss: 1.8545 - val_accuracy: 0.3330\n",
            "Epoch 12/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.8459 - accuracy: 0.3101 - val_loss: 1.8112 - val_accuracy: 0.3400\n",
            "Epoch 13/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.8320 - accuracy: 0.3168 - val_loss: 1.8487 - val_accuracy: 0.3090\n",
            "Epoch 14/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.8206 - accuracy: 0.3204 - val_loss: 1.7979 - val_accuracy: 0.3220\n",
            "Epoch 15/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.8099 - accuracy: 0.3233 - val_loss: 1.7973 - val_accuracy: 0.3440\n",
            "Epoch 16/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.7965 - accuracy: 0.3306 - val_loss: 1.7780 - val_accuracy: 0.3465\n",
            "Epoch 17/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.7833 - accuracy: 0.3329 - val_loss: 1.7771 - val_accuracy: 0.3470\n",
            "Epoch 18/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.7747 - accuracy: 0.3373 - val_loss: 1.7747 - val_accuracy: 0.3230\n",
            "Epoch 19/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.7699 - accuracy: 0.3377 - val_loss: 1.7312 - val_accuracy: 0.3530\n",
            "Epoch 20/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.7580 - accuracy: 0.3433 - val_loss: 1.7383 - val_accuracy: 0.3595\n",
            "Epoch 21/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.7536 - accuracy: 0.3510 - val_loss: 1.7269 - val_accuracy: 0.3600\n",
            "Epoch 22/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.7346 - accuracy: 0.3593 - val_loss: 1.7285 - val_accuracy: 0.3600\n",
            "Epoch 23/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.7366 - accuracy: 0.3461 - val_loss: 1.7144 - val_accuracy: 0.3675\n",
            "Epoch 24/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.7160 - accuracy: 0.3607 - val_loss: 1.7221 - val_accuracy: 0.3570\n",
            "Epoch 25/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.7169 - accuracy: 0.3579 - val_loss: 1.6807 - val_accuracy: 0.3825\n",
            "Epoch 26/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.7131 - accuracy: 0.3606 - val_loss: 1.7051 - val_accuracy: 0.3810\n",
            "Epoch 27/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.7016 - accuracy: 0.3608 - val_loss: 1.6852 - val_accuracy: 0.3675\n",
            "Epoch 28/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.6925 - accuracy: 0.3704 - val_loss: 1.6835 - val_accuracy: 0.3645\n",
            "Epoch 29/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.6906 - accuracy: 0.3671 - val_loss: 1.6861 - val_accuracy: 0.3775\n",
            "Epoch 30/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.6832 - accuracy: 0.3792 - val_loss: 1.7343 - val_accuracy: 0.3920\n",
            "Epoch 31/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.6800 - accuracy: 0.3714 - val_loss: 1.6906 - val_accuracy: 0.3645\n",
            "Epoch 32/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.6705 - accuracy: 0.3807 - val_loss: 1.6472 - val_accuracy: 0.3950\n",
            "Epoch 33/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.6584 - accuracy: 0.3841 - val_loss: 1.7975 - val_accuracy: 0.3560\n",
            "Epoch 34/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.6544 - accuracy: 0.3864 - val_loss: 1.6425 - val_accuracy: 0.3910\n",
            "Epoch 35/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.6487 - accuracy: 0.3902 - val_loss: 1.7321 - val_accuracy: 0.3600\n",
            "Epoch 36/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.6413 - accuracy: 0.3949 - val_loss: 1.6738 - val_accuracy: 0.3900\n",
            "Epoch 37/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.6322 - accuracy: 0.3929 - val_loss: 1.6051 - val_accuracy: 0.4045\n",
            "Epoch 38/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.6314 - accuracy: 0.3981 - val_loss: 1.5913 - val_accuracy: 0.4080\n",
            "Epoch 39/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.6209 - accuracy: 0.4071 - val_loss: 1.6510 - val_accuracy: 0.3840\n",
            "Epoch 40/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.6161 - accuracy: 0.3988 - val_loss: 1.6469 - val_accuracy: 0.4075\n",
            "Epoch 41/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.6108 - accuracy: 0.4057 - val_loss: 1.6191 - val_accuracy: 0.4105\n",
            "Epoch 42/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.6058 - accuracy: 0.4130 - val_loss: 1.6660 - val_accuracy: 0.3920\n",
            "Epoch 43/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.5997 - accuracy: 0.4100 - val_loss: 1.5466 - val_accuracy: 0.4295\n",
            "Epoch 44/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5957 - accuracy: 0.4090 - val_loss: 1.5664 - val_accuracy: 0.4320\n",
            "Epoch 45/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5950 - accuracy: 0.4075 - val_loss: 1.5483 - val_accuracy: 0.4200\n",
            "Epoch 46/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.5759 - accuracy: 0.4229 - val_loss: 1.6705 - val_accuracy: 0.3855\n",
            "Epoch 47/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5741 - accuracy: 0.4160 - val_loss: 1.5265 - val_accuracy: 0.4295\n",
            "Epoch 48/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.5705 - accuracy: 0.4189 - val_loss: 1.5356 - val_accuracy: 0.4335\n",
            "Epoch 49/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5644 - accuracy: 0.4234 - val_loss: 1.5178 - val_accuracy: 0.4355\n",
            "Epoch 50/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5504 - accuracy: 0.4301 - val_loss: 1.5403 - val_accuracy: 0.4370\n",
            "Epoch 51/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5460 - accuracy: 0.4348 - val_loss: 1.5995 - val_accuracy: 0.4185\n",
            "Epoch 52/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5508 - accuracy: 0.4366 - val_loss: 1.5467 - val_accuracy: 0.4265\n",
            "Epoch 53/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5427 - accuracy: 0.4391 - val_loss: 1.7656 - val_accuracy: 0.4110\n",
            "Epoch 54/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5397 - accuracy: 0.4445 - val_loss: 1.5506 - val_accuracy: 0.4435\n",
            "Epoch 55/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5227 - accuracy: 0.4422 - val_loss: 1.6467 - val_accuracy: 0.4185\n",
            "Epoch 56/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5263 - accuracy: 0.4407 - val_loss: 1.9011 - val_accuracy: 0.3860\n",
            "Epoch 57/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5175 - accuracy: 0.4459 - val_loss: 1.5102 - val_accuracy: 0.4415\n",
            "Epoch 58/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5075 - accuracy: 0.4539 - val_loss: 1.6750 - val_accuracy: 0.4245\n",
            "Epoch 59/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.5083 - accuracy: 0.4521 - val_loss: 1.5111 - val_accuracy: 0.4405\n",
            "Epoch 60/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5046 - accuracy: 0.4490 - val_loss: 1.4715 - val_accuracy: 0.4590\n",
            "Epoch 61/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.4907 - accuracy: 0.4559 - val_loss: 1.6654 - val_accuracy: 0.3995\n",
            "Epoch 62/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.4966 - accuracy: 0.4457 - val_loss: 1.4883 - val_accuracy: 0.4555\n",
            "Epoch 63/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.4902 - accuracy: 0.4592 - val_loss: 1.5351 - val_accuracy: 0.4475\n",
            "Epoch 64/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.4985 - accuracy: 0.4567 - val_loss: 1.5268 - val_accuracy: 0.4550\n",
            "Epoch 65/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.4850 - accuracy: 0.4586 - val_loss: 1.5177 - val_accuracy: 0.4430\n",
            "Epoch 66/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.4724 - accuracy: 0.4622 - val_loss: 1.4582 - val_accuracy: 0.4625\n",
            "Epoch 67/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.4683 - accuracy: 0.4657 - val_loss: 1.5750 - val_accuracy: 0.4575\n",
            "Epoch 68/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.4693 - accuracy: 0.4630 - val_loss: 1.4418 - val_accuracy: 0.4715\n",
            "Epoch 69/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.4623 - accuracy: 0.4635 - val_loss: 1.4690 - val_accuracy: 0.4590\n",
            "Epoch 70/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.4625 - accuracy: 0.4717 - val_loss: 1.4412 - val_accuracy: 0.4695\n",
            "Epoch 71/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.4508 - accuracy: 0.4690 - val_loss: 1.4752 - val_accuracy: 0.4670\n",
            "Epoch 72/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.4574 - accuracy: 0.4726 - val_loss: 1.4134 - val_accuracy: 0.4685\n",
            "Epoch 73/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.4474 - accuracy: 0.4810 - val_loss: 1.5824 - val_accuracy: 0.4475\n",
            "Epoch 74/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.4424 - accuracy: 0.4757 - val_loss: 1.5130 - val_accuracy: 0.4600\n",
            "Epoch 75/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.4363 - accuracy: 0.4764 - val_loss: 1.4563 - val_accuracy: 0.4590\n",
            "Network takes 219.302 seconds to train\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rM5mFUHkSWcp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "13512530-b7f1-4d8a-d075-2c24a7352e17"
      },
      "source": [
        "# Train model with a decay schedule\n",
        "first_decay_steps = 1000\n",
        "lr_decayed_fn = (\n",
        "  tf.keras.experimental.CosineDecay(\n",
        "      initial_learning_rate=0.1,\n",
        "      decay_steps=first_decay_steps))\n",
        "\n",
        "model = get_training_model()\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=tf.keras.optimizers.SGD(lr_decayed_fn), metrics=[\"accuracy\"])\n",
        "start = time.time()\n",
        "h = model.fit(train_ds,\n",
        "         validation_data=test_ds,\n",
        "         epochs=75)\n",
        "end = time.time()\n",
        "print(\"Network takes {:.3f} seconds to train\".format(end - start))\n",
        "plot_training(h)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/75\n",
            "79/79 [==============================] - 3s 39ms/step - loss: 2.1259 - accuracy: 0.2033 - val_loss: 2.0901 - val_accuracy: 0.1805\n",
            "Epoch 2/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.9193 - accuracy: 0.2812 - val_loss: 2.5864 - val_accuracy: 0.1565\n",
            "Epoch 3/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.8297 - accuracy: 0.3105 - val_loss: 3.1432 - val_accuracy: 0.1460\n",
            "Epoch 4/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.7626 - accuracy: 0.3462 - val_loss: 1.8474 - val_accuracy: 0.2975\n",
            "Epoch 5/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.7062 - accuracy: 0.3700 - val_loss: 1.7207 - val_accuracy: 0.3805\n",
            "Epoch 6/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.6396 - accuracy: 0.3950 - val_loss: 1.7648 - val_accuracy: 0.3760\n",
            "Epoch 7/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.5987 - accuracy: 0.4120 - val_loss: 2.0496 - val_accuracy: 0.3200\n",
            "Epoch 8/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5801 - accuracy: 0.4170 - val_loss: 1.6605 - val_accuracy: 0.4060\n",
            "Epoch 9/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5558 - accuracy: 0.4308 - val_loss: 1.5274 - val_accuracy: 0.4410\n",
            "Epoch 10/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5411 - accuracy: 0.4383 - val_loss: 1.4741 - val_accuracy: 0.4550\n",
            "Epoch 11/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5117 - accuracy: 0.4457 - val_loss: 1.4530 - val_accuracy: 0.4685\n",
            "Epoch 12/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5035 - accuracy: 0.4491 - val_loss: 1.4443 - val_accuracy: 0.4745\n",
            "Epoch 13/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5065 - accuracy: 0.4474 - val_loss: 1.4448 - val_accuracy: 0.4740\n",
            "Epoch 14/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.5176 - accuracy: 0.4478 - val_loss: 1.4452 - val_accuracy: 0.4725\n",
            "Epoch 15/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5124 - accuracy: 0.4486 - val_loss: 1.4452 - val_accuracy: 0.4720\n",
            "Epoch 16/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5136 - accuracy: 0.4446 - val_loss: 1.4452 - val_accuracy: 0.4715\n",
            "Epoch 17/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5106 - accuracy: 0.4501 - val_loss: 1.4451 - val_accuracy: 0.4705\n",
            "Epoch 18/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.5003 - accuracy: 0.4498 - val_loss: 1.4450 - val_accuracy: 0.4705\n",
            "Epoch 19/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5105 - accuracy: 0.4430 - val_loss: 1.4451 - val_accuracy: 0.4710\n",
            "Epoch 20/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5066 - accuracy: 0.4455 - val_loss: 1.4453 - val_accuracy: 0.4705\n",
            "Epoch 21/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5161 - accuracy: 0.4537 - val_loss: 1.4452 - val_accuracy: 0.4715\n",
            "Epoch 22/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.5141 - accuracy: 0.4439 - val_loss: 1.4456 - val_accuracy: 0.4705\n",
            "Epoch 23/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.5162 - accuracy: 0.4426 - val_loss: 1.4460 - val_accuracy: 0.4700\n",
            "Epoch 24/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.5199 - accuracy: 0.4484 - val_loss: 1.4458 - val_accuracy: 0.4700\n",
            "Epoch 25/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5074 - accuracy: 0.4504 - val_loss: 1.4454 - val_accuracy: 0.4705\n",
            "Epoch 26/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.5115 - accuracy: 0.4503 - val_loss: 1.4457 - val_accuracy: 0.4710\n",
            "Epoch 27/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5054 - accuracy: 0.4449 - val_loss: 1.4461 - val_accuracy: 0.4705\n",
            "Epoch 28/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5063 - accuracy: 0.4499 - val_loss: 1.4456 - val_accuracy: 0.4715\n",
            "Epoch 29/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5043 - accuracy: 0.4461 - val_loss: 1.4455 - val_accuracy: 0.4710\n",
            "Epoch 30/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.5002 - accuracy: 0.4565 - val_loss: 1.4456 - val_accuracy: 0.4700\n",
            "Epoch 31/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.5088 - accuracy: 0.4502 - val_loss: 1.4454 - val_accuracy: 0.4705\n",
            "Epoch 32/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.5129 - accuracy: 0.4498 - val_loss: 1.4454 - val_accuracy: 0.4705\n",
            "Epoch 33/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5062 - accuracy: 0.4522 - val_loss: 1.4453 - val_accuracy: 0.4710\n",
            "Epoch 34/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5086 - accuracy: 0.4530 - val_loss: 1.4455 - val_accuracy: 0.4710\n",
            "Epoch 35/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5088 - accuracy: 0.4498 - val_loss: 1.4453 - val_accuracy: 0.4715\n",
            "Epoch 36/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.5160 - accuracy: 0.4424 - val_loss: 1.4455 - val_accuracy: 0.4715\n",
            "Epoch 37/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5182 - accuracy: 0.4437 - val_loss: 1.4455 - val_accuracy: 0.4715\n",
            "Epoch 38/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5157 - accuracy: 0.4399 - val_loss: 1.4454 - val_accuracy: 0.4710\n",
            "Epoch 39/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.5096 - accuracy: 0.4478 - val_loss: 1.4452 - val_accuracy: 0.4715\n",
            "Epoch 40/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5107 - accuracy: 0.4495 - val_loss: 1.4452 - val_accuracy: 0.4710\n",
            "Epoch 41/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5050 - accuracy: 0.4532 - val_loss: 1.4455 - val_accuracy: 0.4710\n",
            "Epoch 42/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5073 - accuracy: 0.4487 - val_loss: 1.4455 - val_accuracy: 0.4720\n",
            "Epoch 43/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5099 - accuracy: 0.4551 - val_loss: 1.4455 - val_accuracy: 0.4700\n",
            "Epoch 44/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5101 - accuracy: 0.4496 - val_loss: 1.4458 - val_accuracy: 0.4700\n",
            "Epoch 45/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5052 - accuracy: 0.4499 - val_loss: 1.4458 - val_accuracy: 0.4715\n",
            "Epoch 46/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5076 - accuracy: 0.4506 - val_loss: 1.4456 - val_accuracy: 0.4700\n",
            "Epoch 47/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5088 - accuracy: 0.4427 - val_loss: 1.4453 - val_accuracy: 0.4715\n",
            "Epoch 48/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5132 - accuracy: 0.4548 - val_loss: 1.4451 - val_accuracy: 0.4725\n",
            "Epoch 49/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5071 - accuracy: 0.4520 - val_loss: 1.4451 - val_accuracy: 0.4705\n",
            "Epoch 50/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5100 - accuracy: 0.4466 - val_loss: 1.4455 - val_accuracy: 0.4685\n",
            "Epoch 51/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.5096 - accuracy: 0.4517 - val_loss: 1.4457 - val_accuracy: 0.4705\n",
            "Epoch 52/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5157 - accuracy: 0.4487 - val_loss: 1.4456 - val_accuracy: 0.4710\n",
            "Epoch 53/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.5108 - accuracy: 0.4484 - val_loss: 1.4457 - val_accuracy: 0.4705\n",
            "Epoch 54/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5072 - accuracy: 0.4492 - val_loss: 1.4457 - val_accuracy: 0.4700\n",
            "Epoch 55/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5183 - accuracy: 0.4491 - val_loss: 1.4452 - val_accuracy: 0.4725\n",
            "Epoch 56/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.5064 - accuracy: 0.4567 - val_loss: 1.4453 - val_accuracy: 0.4720\n",
            "Epoch 57/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.5049 - accuracy: 0.4538 - val_loss: 1.4451 - val_accuracy: 0.4725\n",
            "Epoch 58/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5078 - accuracy: 0.4549 - val_loss: 1.4452 - val_accuracy: 0.4725\n",
            "Epoch 59/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5059 - accuracy: 0.4486 - val_loss: 1.4455 - val_accuracy: 0.4720\n",
            "Epoch 60/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5160 - accuracy: 0.4534 - val_loss: 1.4457 - val_accuracy: 0.4705\n",
            "Epoch 61/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5021 - accuracy: 0.4567 - val_loss: 1.4454 - val_accuracy: 0.4710\n",
            "Epoch 62/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5044 - accuracy: 0.4499 - val_loss: 1.4454 - val_accuracy: 0.4700\n",
            "Epoch 63/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5109 - accuracy: 0.4490 - val_loss: 1.4457 - val_accuracy: 0.4705\n",
            "Epoch 64/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5049 - accuracy: 0.4595 - val_loss: 1.4457 - val_accuracy: 0.4705\n",
            "Epoch 65/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5094 - accuracy: 0.4469 - val_loss: 1.4456 - val_accuracy: 0.4710\n",
            "Epoch 66/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5069 - accuracy: 0.4486 - val_loss: 1.4456 - val_accuracy: 0.4700\n",
            "Epoch 67/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.5041 - accuracy: 0.4509 - val_loss: 1.4458 - val_accuracy: 0.4705\n",
            "Epoch 68/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5084 - accuracy: 0.4523 - val_loss: 1.4455 - val_accuracy: 0.4725\n",
            "Epoch 69/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5168 - accuracy: 0.4455 - val_loss: 1.4455 - val_accuracy: 0.4720\n",
            "Epoch 70/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.5146 - accuracy: 0.4474 - val_loss: 1.4456 - val_accuracy: 0.4715\n",
            "Epoch 71/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5039 - accuracy: 0.4448 - val_loss: 1.4458 - val_accuracy: 0.4710\n",
            "Epoch 72/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5056 - accuracy: 0.4435 - val_loss: 1.4455 - val_accuracy: 0.4715\n",
            "Epoch 73/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5114 - accuracy: 0.4473 - val_loss: 1.4453 - val_accuracy: 0.4715\n",
            "Epoch 74/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5150 - accuracy: 0.4467 - val_loss: 1.4452 - val_accuracy: 0.4705\n",
            "Epoch 75/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5162 - accuracy: 0.4467 - val_loss: 1.4455 - val_accuracy: 0.4725\n",
            "Network takes 217.771 seconds to train\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SLFNETnFZL_J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e7801664-7a16-4cbf-cbe8-c091bba9763f"
      },
      "source": [
        "# Train model with a different decay schedule\n",
        "first_decay_steps = 1000\n",
        "lr_decayed_fn = (\n",
        "  tf.keras.experimental.LinearCosineDecay(\n",
        "      initial_learning_rate=0.1,\n",
        "      decay_steps=first_decay_steps))\n",
        "\n",
        "model = get_training_model()\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=tf.keras.optimizers.SGD(lr_decayed_fn), metrics=[\"accuracy\"])\n",
        "start = time.time()\n",
        "h = model.fit(train_ds,\n",
        "         validation_data=test_ds,\n",
        "         epochs=75)\n",
        "end = time.time()\n",
        "print(\"Network takes {:.3f} seconds to train\".format(end - start))\n",
        "plot_training(h)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/75\n",
            "79/79 [==============================] - 3s 40ms/step - loss: 2.0763 - accuracy: 0.2206 - val_loss: 2.1847 - val_accuracy: 0.1725\n",
            "Epoch 2/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.8929 - accuracy: 0.2943 - val_loss: 2.0039 - val_accuracy: 0.2250\n",
            "Epoch 3/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.8104 - accuracy: 0.3267 - val_loss: 2.2286 - val_accuracy: 0.1950\n",
            "Epoch 4/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.7515 - accuracy: 0.3465 - val_loss: 2.1538 - val_accuracy: 0.2305\n",
            "Epoch 5/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.7022 - accuracy: 0.3715 - val_loss: 1.6909 - val_accuracy: 0.3870\n",
            "Epoch 6/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.6727 - accuracy: 0.3838 - val_loss: 2.1626 - val_accuracy: 0.3055\n",
            "Epoch 7/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.6549 - accuracy: 0.3966 - val_loss: 1.8766 - val_accuracy: 0.3390\n",
            "Epoch 8/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.6275 - accuracy: 0.4063 - val_loss: 1.5862 - val_accuracy: 0.4240\n",
            "Epoch 9/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.6136 - accuracy: 0.4060 - val_loss: 1.5503 - val_accuracy: 0.4415\n",
            "Epoch 10/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.5994 - accuracy: 0.4087 - val_loss: 1.5394 - val_accuracy: 0.4390\n",
            "Epoch 11/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.5957 - accuracy: 0.4138 - val_loss: 1.5384 - val_accuracy: 0.4310\n",
            "Epoch 12/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.6015 - accuracy: 0.4177 - val_loss: 1.5378 - val_accuracy: 0.4310\n",
            "Epoch 13/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5913 - accuracy: 0.4201 - val_loss: 1.5382 - val_accuracy: 0.4335\n",
            "Epoch 14/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5894 - accuracy: 0.4152 - val_loss: 1.5368 - val_accuracy: 0.4335\n",
            "Epoch 15/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.5961 - accuracy: 0.4157 - val_loss: 1.5370 - val_accuracy: 0.4325\n",
            "Epoch 16/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.6040 - accuracy: 0.4144 - val_loss: 1.5383 - val_accuracy: 0.4315\n",
            "Epoch 17/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.6018 - accuracy: 0.4172 - val_loss: 1.5383 - val_accuracy: 0.4315\n",
            "Epoch 18/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5883 - accuracy: 0.4220 - val_loss: 1.5383 - val_accuracy: 0.4325\n",
            "Epoch 19/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.6053 - accuracy: 0.4151 - val_loss: 1.5378 - val_accuracy: 0.4325\n",
            "Epoch 20/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.5995 - accuracy: 0.4127 - val_loss: 1.5372 - val_accuracy: 0.4340\n",
            "Epoch 21/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5956 - accuracy: 0.4163 - val_loss: 1.5379 - val_accuracy: 0.4355\n",
            "Epoch 22/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.5999 - accuracy: 0.4186 - val_loss: 1.5379 - val_accuracy: 0.4350\n",
            "Epoch 23/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5949 - accuracy: 0.4220 - val_loss: 1.5382 - val_accuracy: 0.4345\n",
            "Epoch 24/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.5973 - accuracy: 0.4237 - val_loss: 1.5385 - val_accuracy: 0.4340\n",
            "Epoch 25/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.6070 - accuracy: 0.4161 - val_loss: 1.5387 - val_accuracy: 0.4330\n",
            "Epoch 26/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5963 - accuracy: 0.4196 - val_loss: 1.5388 - val_accuracy: 0.4350\n",
            "Epoch 27/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5969 - accuracy: 0.4178 - val_loss: 1.5384 - val_accuracy: 0.4325\n",
            "Epoch 28/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.6015 - accuracy: 0.4155 - val_loss: 1.5383 - val_accuracy: 0.4330\n",
            "Epoch 29/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5965 - accuracy: 0.4185 - val_loss: 1.5375 - val_accuracy: 0.4340\n",
            "Epoch 30/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.5979 - accuracy: 0.4190 - val_loss: 1.5374 - val_accuracy: 0.4365\n",
            "Epoch 31/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.5960 - accuracy: 0.4228 - val_loss: 1.5381 - val_accuracy: 0.4350\n",
            "Epoch 32/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.6002 - accuracy: 0.4200 - val_loss: 1.5378 - val_accuracy: 0.4340\n",
            "Epoch 33/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.5981 - accuracy: 0.4169 - val_loss: 1.5375 - val_accuracy: 0.4345\n",
            "Epoch 34/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.5965 - accuracy: 0.4192 - val_loss: 1.5379 - val_accuracy: 0.4345\n",
            "Epoch 35/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5964 - accuracy: 0.4183 - val_loss: 1.5379 - val_accuracy: 0.4340\n",
            "Epoch 36/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5965 - accuracy: 0.4174 - val_loss: 1.5381 - val_accuracy: 0.4335\n",
            "Epoch 37/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5958 - accuracy: 0.4248 - val_loss: 1.5372 - val_accuracy: 0.4345\n",
            "Epoch 38/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.6020 - accuracy: 0.4179 - val_loss: 1.5371 - val_accuracy: 0.4360\n",
            "Epoch 39/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5954 - accuracy: 0.4191 - val_loss: 1.5372 - val_accuracy: 0.4350\n",
            "Epoch 40/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.6004 - accuracy: 0.4122 - val_loss: 1.5378 - val_accuracy: 0.4340\n",
            "Epoch 41/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5980 - accuracy: 0.4185 - val_loss: 1.5373 - val_accuracy: 0.4340\n",
            "Epoch 42/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5899 - accuracy: 0.4187 - val_loss: 1.5371 - val_accuracy: 0.4340\n",
            "Epoch 43/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.6003 - accuracy: 0.4207 - val_loss: 1.5371 - val_accuracy: 0.4355\n",
            "Epoch 44/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5930 - accuracy: 0.4220 - val_loss: 1.5370 - val_accuracy: 0.4355\n",
            "Epoch 45/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.6039 - accuracy: 0.4148 - val_loss: 1.5373 - val_accuracy: 0.4340\n",
            "Epoch 46/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5986 - accuracy: 0.4224 - val_loss: 1.5373 - val_accuracy: 0.4350\n",
            "Epoch 47/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5938 - accuracy: 0.4168 - val_loss: 1.5370 - val_accuracy: 0.4350\n",
            "Epoch 48/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.6024 - accuracy: 0.4185 - val_loss: 1.5371 - val_accuracy: 0.4350\n",
            "Epoch 49/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.6035 - accuracy: 0.4141 - val_loss: 1.5364 - val_accuracy: 0.4365\n",
            "Epoch 50/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5977 - accuracy: 0.4155 - val_loss: 1.5369 - val_accuracy: 0.4360\n",
            "Epoch 51/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5987 - accuracy: 0.4190 - val_loss: 1.5373 - val_accuracy: 0.4365\n",
            "Epoch 52/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.6028 - accuracy: 0.4231 - val_loss: 1.5373 - val_accuracy: 0.4380\n",
            "Epoch 53/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5887 - accuracy: 0.4210 - val_loss: 1.5363 - val_accuracy: 0.4365\n",
            "Epoch 54/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5942 - accuracy: 0.4194 - val_loss: 1.5362 - val_accuracy: 0.4365\n",
            "Epoch 55/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5909 - accuracy: 0.4223 - val_loss: 1.5363 - val_accuracy: 0.4385\n",
            "Epoch 56/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5931 - accuracy: 0.4223 - val_loss: 1.5365 - val_accuracy: 0.4365\n",
            "Epoch 57/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5992 - accuracy: 0.4145 - val_loss: 1.5360 - val_accuracy: 0.4360\n",
            "Epoch 58/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5924 - accuracy: 0.4183 - val_loss: 1.5367 - val_accuracy: 0.4380\n",
            "Epoch 59/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.5949 - accuracy: 0.4175 - val_loss: 1.5372 - val_accuracy: 0.4360\n",
            "Epoch 60/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.6001 - accuracy: 0.4183 - val_loss: 1.5363 - val_accuracy: 0.4375\n",
            "Epoch 61/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5971 - accuracy: 0.4147 - val_loss: 1.5357 - val_accuracy: 0.4375\n",
            "Epoch 62/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5980 - accuracy: 0.4169 - val_loss: 1.5359 - val_accuracy: 0.4375\n",
            "Epoch 63/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5963 - accuracy: 0.4148 - val_loss: 1.5361 - val_accuracy: 0.4380\n",
            "Epoch 64/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5858 - accuracy: 0.4252 - val_loss: 1.5356 - val_accuracy: 0.4365\n",
            "Epoch 65/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.5960 - accuracy: 0.4193 - val_loss: 1.5353 - val_accuracy: 0.4360\n",
            "Epoch 66/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5936 - accuracy: 0.4180 - val_loss: 1.5368 - val_accuracy: 0.4375\n",
            "Epoch 67/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5911 - accuracy: 0.4162 - val_loss: 1.5353 - val_accuracy: 0.4365\n",
            "Epoch 68/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5947 - accuracy: 0.4196 - val_loss: 1.5357 - val_accuracy: 0.4380\n",
            "Epoch 69/75\n",
            "79/79 [==============================] - 3s 36ms/step - loss: 1.6000 - accuracy: 0.4192 - val_loss: 1.5352 - val_accuracy: 0.4365\n",
            "Epoch 70/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5891 - accuracy: 0.4220 - val_loss: 1.5351 - val_accuracy: 0.4385\n",
            "Epoch 71/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.6018 - accuracy: 0.4170 - val_loss: 1.5350 - val_accuracy: 0.4385\n",
            "Epoch 72/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.6005 - accuracy: 0.4176 - val_loss: 1.5348 - val_accuracy: 0.4375\n",
            "Epoch 73/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5963 - accuracy: 0.4155 - val_loss: 1.5352 - val_accuracy: 0.4380\n",
            "Epoch 74/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5937 - accuracy: 0.4203 - val_loss: 1.5347 - val_accuracy: 0.4375\n",
            "Epoch 75/75\n",
            "79/79 [==============================] - 3s 35ms/step - loss: 1.5989 - accuracy: 0.4167 - val_loss: 1.5359 - val_accuracy: 0.4375\n",
            "Network takes 217.699 seconds to train\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ou8GN5VhWrbd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "671ad91e-b94c-40f7-f4db-225cc4b8f541"
      },
      "source": [
        "model = get_training_model()\n",
        "\n",
        "for layer in model.layers:\n",
        "    if not isinstance(layer, tf.keras.layers.BatchNormalization):\n",
        "        if hasattr(layer, \"trainable\"):\n",
        "            layer.trainable=False\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_9\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_10 (InputLayer)           [(None, 32, 32, 3)]  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_342 (Conv2D)             (None, 32, 32, 16)   448         input_10[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_324 (BatchN (None, 32, 32, 16)   64          conv2d_342[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_324 (ReLU)                (None, 32, 32, 16)   0           batch_normalization_324[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_344 (Conv2D)             (None, 32, 32, 16)   272         re_lu_324[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_325 (BatchN (None, 32, 32, 16)   64          conv2d_344[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_325 (ReLU)                (None, 32, 32, 16)   0           batch_normalization_325[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_345 (Conv2D)             (None, 32, 32, 16)   2320        re_lu_325[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_326 (BatchN (None, 32, 32, 16)   64          conv2d_345[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_326 (ReLU)                (None, 32, 32, 16)   0           batch_normalization_326[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_346 (Conv2D)             (None, 32, 32, 64)   1088        re_lu_326[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_343 (Conv2D)             (None, 32, 32, 64)   1088        re_lu_324[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_327 (BatchN (None, 32, 32, 64)   256         conv2d_346[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "add_102 (Add)                   (None, 32, 32, 64)   0           conv2d_343[0][0]                 \n",
            "                                                                 batch_normalization_327[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_327 (ReLU)                (None, 32, 32, 64)   0           add_102[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_347 (Conv2D)             (None, 32, 32, 16)   1040        re_lu_327[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_328 (BatchN (None, 32, 32, 16)   64          conv2d_347[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_328 (ReLU)                (None, 32, 32, 16)   0           batch_normalization_328[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_348 (Conv2D)             (None, 32, 32, 16)   2320        re_lu_328[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_329 (BatchN (None, 32, 32, 16)   64          conv2d_348[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_329 (ReLU)                (None, 32, 32, 16)   0           batch_normalization_329[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_349 (Conv2D)             (None, 32, 32, 64)   1088        re_lu_329[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_330 (BatchN (None, 32, 32, 64)   256         conv2d_349[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "add_103 (Add)                   (None, 32, 32, 64)   0           batch_normalization_330[0][0]    \n",
            "                                                                 re_lu_327[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_330 (ReLU)                (None, 32, 32, 64)   0           add_103[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_351 (Conv2D)             (None, 32, 32, 64)   4160        re_lu_330[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_331 (BatchN (None, 32, 32, 64)   256         conv2d_351[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_331 (ReLU)                (None, 32, 32, 64)   0           batch_normalization_331[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_352 (Conv2D)             (None, 16, 16, 64)   36928       re_lu_331[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_332 (BatchN (None, 16, 16, 64)   256         conv2d_352[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_332 (ReLU)                (None, 16, 16, 64)   0           batch_normalization_332[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_353 (Conv2D)             (None, 16, 16, 128)  8320        re_lu_332[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_350 (Conv2D)             (None, 16, 16, 128)  8320        re_lu_330[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_333 (BatchN (None, 16, 16, 128)  512         conv2d_353[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "add_104 (Add)                   (None, 16, 16, 128)  0           conv2d_350[0][0]                 \n",
            "                                                                 batch_normalization_333[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_333 (ReLU)                (None, 16, 16, 128)  0           add_104[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_354 (Conv2D)             (None, 16, 16, 64)   8256        re_lu_333[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_334 (BatchN (None, 16, 16, 64)   256         conv2d_354[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_334 (ReLU)                (None, 16, 16, 64)   0           batch_normalization_334[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_355 (Conv2D)             (None, 16, 16, 64)   36928       re_lu_334[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_335 (BatchN (None, 16, 16, 64)   256         conv2d_355[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_335 (ReLU)                (None, 16, 16, 64)   0           batch_normalization_335[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_356 (Conv2D)             (None, 16, 16, 128)  8320        re_lu_335[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_336 (BatchN (None, 16, 16, 128)  512         conv2d_356[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "add_105 (Add)                   (None, 16, 16, 128)  0           batch_normalization_336[0][0]    \n",
            "                                                                 re_lu_333[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_336 (ReLU)                (None, 16, 16, 128)  0           add_105[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_358 (Conv2D)             (None, 16, 16, 128)  16512       re_lu_336[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_337 (BatchN (None, 16, 16, 128)  512         conv2d_358[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_337 (ReLU)                (None, 16, 16, 128)  0           batch_normalization_337[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_359 (Conv2D)             (None, 8, 8, 128)    147584      re_lu_337[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_338 (BatchN (None, 8, 8, 128)    512         conv2d_359[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_338 (ReLU)                (None, 8, 8, 128)    0           batch_normalization_338[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_360 (Conv2D)             (None, 8, 8, 256)    33024       re_lu_338[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_357 (Conv2D)             (None, 8, 8, 256)    33024       re_lu_336[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_339 (BatchN (None, 8, 8, 256)    1024        conv2d_360[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "add_106 (Add)                   (None, 8, 8, 256)    0           conv2d_357[0][0]                 \n",
            "                                                                 batch_normalization_339[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_339 (ReLU)                (None, 8, 8, 256)    0           add_106[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_361 (Conv2D)             (None, 8, 8, 128)    32896       re_lu_339[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_340 (BatchN (None, 8, 8, 128)    512         conv2d_361[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_340 (ReLU)                (None, 8, 8, 128)    0           batch_normalization_340[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_362 (Conv2D)             (None, 8, 8, 128)    147584      re_lu_340[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_341 (BatchN (None, 8, 8, 128)    512         conv2d_362[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_341 (ReLU)                (None, 8, 8, 128)    0           batch_normalization_341[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_363 (Conv2D)             (None, 8, 8, 256)    33024       re_lu_341[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_342 (BatchN (None, 8, 8, 256)    1024        conv2d_363[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "add_107 (Add)                   (None, 8, 8, 256)    0           batch_normalization_342[0][0]    \n",
            "                                                                 re_lu_339[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_342 (ReLU)                (None, 8, 8, 256)    0           add_107[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_343 (BatchN (None, 8, 8, 256)    1024        re_lu_342[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_343 (ReLU)                (None, 8, 8, 256)    0           batch_normalization_343[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_9 (AveragePoo (None, 1, 1, 256)    0           re_lu_343[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "flatten_9 (Flatten)             (None, 256)          0           average_pooling2d_9[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "dense_9 (Dense)                 (None, 10)           2570        flatten_9[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 575,114\n",
            "Trainable params: 4,000\n",
            "Non-trainable params: 571,114\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q0pgz6fvajZV",
        "colab_type": "text"
      },
      "source": [
        "- Total params: 575,114\n",
        "- Trainable params: 4,000\n",
        "- Non-trainable params: 571,114"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HpzMXab8Us7C",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8de0f35b-bc39-48a4-a74d-cb41b01f13a1"
      },
      "source": [
        "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"sgd\", metrics=[\"accuracy\"])\n",
        "start = time.time()\n",
        "h = model.fit(train_ds,\n",
        "         validation_data=test_ds,\n",
        "         epochs=75)\n",
        "end = time.time()\n",
        "print(\"Network takes {:.3f} seconds to train\".format(end - start))\n",
        "plot_training(h)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/75\n",
            "79/79 [==============================] - 3s 32ms/step - loss: 2.4319 - accuracy: 0.1034 - val_loss: 2.3069 - val_accuracy: 0.1430\n",
            "Epoch 2/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.4188 - accuracy: 0.1041 - val_loss: 2.3108 - val_accuracy: 0.1500\n",
            "Epoch 3/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.4152 - accuracy: 0.1042 - val_loss: 2.3181 - val_accuracy: 0.1365\n",
            "Epoch 4/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.3999 - accuracy: 0.0999 - val_loss: 2.3247 - val_accuracy: 0.1130\n",
            "Epoch 5/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.3886 - accuracy: 0.1065 - val_loss: 2.3264 - val_accuracy: 0.1175\n",
            "Epoch 6/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.3766 - accuracy: 0.1091 - val_loss: 2.3262 - val_accuracy: 0.1205\n",
            "Epoch 7/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.3725 - accuracy: 0.1132 - val_loss: 2.3235 - val_accuracy: 0.1200\n",
            "Epoch 8/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.3593 - accuracy: 0.1086 - val_loss: 2.3203 - val_accuracy: 0.1215\n",
            "Epoch 9/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.3542 - accuracy: 0.1089 - val_loss: 2.3165 - val_accuracy: 0.1245\n",
            "Epoch 10/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.3452 - accuracy: 0.1151 - val_loss: 2.3111 - val_accuracy: 0.1250\n",
            "Epoch 11/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.3380 - accuracy: 0.1126 - val_loss: 2.3063 - val_accuracy: 0.1265\n",
            "Epoch 12/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.3348 - accuracy: 0.1130 - val_loss: 2.3013 - val_accuracy: 0.1330\n",
            "Epoch 13/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.3301 - accuracy: 0.1204 - val_loss: 2.2968 - val_accuracy: 0.1325\n",
            "Epoch 14/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.3208 - accuracy: 0.1178 - val_loss: 2.2928 - val_accuracy: 0.1370\n",
            "Epoch 15/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.3186 - accuracy: 0.1209 - val_loss: 2.2888 - val_accuracy: 0.1415\n",
            "Epoch 16/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.3154 - accuracy: 0.1216 - val_loss: 2.2845 - val_accuracy: 0.1480\n",
            "Epoch 17/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.3111 - accuracy: 0.1298 - val_loss: 2.2808 - val_accuracy: 0.1535\n",
            "Epoch 18/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.3047 - accuracy: 0.1214 - val_loss: 2.2773 - val_accuracy: 0.1570\n",
            "Epoch 19/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2975 - accuracy: 0.1302 - val_loss: 2.2741 - val_accuracy: 0.1620\n",
            "Epoch 20/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.3032 - accuracy: 0.1299 - val_loss: 2.2706 - val_accuracy: 0.1660\n",
            "Epoch 21/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2980 - accuracy: 0.1292 - val_loss: 2.2672 - val_accuracy: 0.1710\n",
            "Epoch 22/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.2907 - accuracy: 0.1326 - val_loss: 2.2641 - val_accuracy: 0.1740\n",
            "Epoch 23/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2855 - accuracy: 0.1395 - val_loss: 2.2612 - val_accuracy: 0.1755\n",
            "Epoch 24/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2849 - accuracy: 0.1421 - val_loss: 2.2583 - val_accuracy: 0.1780\n",
            "Epoch 25/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2802 - accuracy: 0.1383 - val_loss: 2.2558 - val_accuracy: 0.1785\n",
            "Epoch 26/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2794 - accuracy: 0.1410 - val_loss: 2.2538 - val_accuracy: 0.1790\n",
            "Epoch 27/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2744 - accuracy: 0.1468 - val_loss: 2.2510 - val_accuracy: 0.1830\n",
            "Epoch 28/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2703 - accuracy: 0.1409 - val_loss: 2.2492 - val_accuracy: 0.1850\n",
            "Epoch 29/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2737 - accuracy: 0.1500 - val_loss: 2.2465 - val_accuracy: 0.1840\n",
            "Epoch 30/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.2698 - accuracy: 0.1483 - val_loss: 2.2445 - val_accuracy: 0.1830\n",
            "Epoch 31/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2692 - accuracy: 0.1482 - val_loss: 2.2422 - val_accuracy: 0.1900\n",
            "Epoch 32/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.2646 - accuracy: 0.1447 - val_loss: 2.2400 - val_accuracy: 0.1875\n",
            "Epoch 33/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.2620 - accuracy: 0.1496 - val_loss: 2.2380 - val_accuracy: 0.1900\n",
            "Epoch 34/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2564 - accuracy: 0.1603 - val_loss: 2.2358 - val_accuracy: 0.1930\n",
            "Epoch 35/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.2576 - accuracy: 0.1614 - val_loss: 2.2339 - val_accuracy: 0.1910\n",
            "Epoch 36/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2557 - accuracy: 0.1567 - val_loss: 2.2319 - val_accuracy: 0.1875\n",
            "Epoch 37/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2534 - accuracy: 0.1545 - val_loss: 2.2303 - val_accuracy: 0.1915\n",
            "Epoch 38/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2550 - accuracy: 0.1620 - val_loss: 2.2283 - val_accuracy: 0.1915\n",
            "Epoch 39/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2452 - accuracy: 0.1637 - val_loss: 2.2262 - val_accuracy: 0.1915\n",
            "Epoch 40/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2432 - accuracy: 0.1663 - val_loss: 2.2245 - val_accuracy: 0.1905\n",
            "Epoch 41/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2468 - accuracy: 0.1613 - val_loss: 2.2230 - val_accuracy: 0.1930\n",
            "Epoch 42/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2410 - accuracy: 0.1698 - val_loss: 2.2213 - val_accuracy: 0.1950\n",
            "Epoch 43/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2435 - accuracy: 0.1714 - val_loss: 2.2195 - val_accuracy: 0.1980\n",
            "Epoch 44/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2382 - accuracy: 0.1683 - val_loss: 2.2181 - val_accuracy: 0.1970\n",
            "Epoch 45/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2367 - accuracy: 0.1654 - val_loss: 2.2164 - val_accuracy: 0.1980\n",
            "Epoch 46/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2345 - accuracy: 0.1697 - val_loss: 2.2149 - val_accuracy: 0.2010\n",
            "Epoch 47/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.2416 - accuracy: 0.1609 - val_loss: 2.2131 - val_accuracy: 0.1990\n",
            "Epoch 48/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2316 - accuracy: 0.1692 - val_loss: 2.2115 - val_accuracy: 0.1970\n",
            "Epoch 49/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2325 - accuracy: 0.1659 - val_loss: 2.2098 - val_accuracy: 0.1975\n",
            "Epoch 50/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2331 - accuracy: 0.1729 - val_loss: 2.2083 - val_accuracy: 0.1980\n",
            "Epoch 51/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.2299 - accuracy: 0.1681 - val_loss: 2.2065 - val_accuracy: 0.2005\n",
            "Epoch 52/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.2321 - accuracy: 0.1726 - val_loss: 2.2047 - val_accuracy: 0.1985\n",
            "Epoch 53/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2269 - accuracy: 0.1714 - val_loss: 2.2034 - val_accuracy: 0.1985\n",
            "Epoch 54/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.2219 - accuracy: 0.1767 - val_loss: 2.2019 - val_accuracy: 0.1995\n",
            "Epoch 55/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.2232 - accuracy: 0.1726 - val_loss: 2.2001 - val_accuracy: 0.1990\n",
            "Epoch 56/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2232 - accuracy: 0.1726 - val_loss: 2.1988 - val_accuracy: 0.1995\n",
            "Epoch 57/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2190 - accuracy: 0.1757 - val_loss: 2.1971 - val_accuracy: 0.1995\n",
            "Epoch 58/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2216 - accuracy: 0.1647 - val_loss: 2.1953 - val_accuracy: 0.1995\n",
            "Epoch 59/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2151 - accuracy: 0.1737 - val_loss: 2.1940 - val_accuracy: 0.1975\n",
            "Epoch 60/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.2143 - accuracy: 0.1766 - val_loss: 2.1924 - val_accuracy: 0.2000\n",
            "Epoch 61/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2128 - accuracy: 0.1778 - val_loss: 2.1908 - val_accuracy: 0.1985\n",
            "Epoch 62/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2103 - accuracy: 0.1794 - val_loss: 2.1892 - val_accuracy: 0.1980\n",
            "Epoch 63/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2103 - accuracy: 0.1772 - val_loss: 2.1880 - val_accuracy: 0.2015\n",
            "Epoch 64/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2084 - accuracy: 0.1803 - val_loss: 2.1865 - val_accuracy: 0.2005\n",
            "Epoch 65/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2084 - accuracy: 0.1774 - val_loss: 2.1851 - val_accuracy: 0.2010\n",
            "Epoch 66/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2084 - accuracy: 0.1730 - val_loss: 2.1843 - val_accuracy: 0.2005\n",
            "Epoch 67/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2084 - accuracy: 0.1792 - val_loss: 2.1823 - val_accuracy: 0.2010\n",
            "Epoch 68/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.2002 - accuracy: 0.1816 - val_loss: 2.1809 - val_accuracy: 0.2010\n",
            "Epoch 69/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.1985 - accuracy: 0.1788 - val_loss: 2.1792 - val_accuracy: 0.2030\n",
            "Epoch 70/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2054 - accuracy: 0.1787 - val_loss: 2.1777 - val_accuracy: 0.2020\n",
            "Epoch 71/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.1990 - accuracy: 0.1786 - val_loss: 2.1770 - val_accuracy: 0.2030\n",
            "Epoch 72/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.1985 - accuracy: 0.1825 - val_loss: 2.1760 - val_accuracy: 0.2020\n",
            "Epoch 73/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.1968 - accuracy: 0.1816 - val_loss: 2.1742 - val_accuracy: 0.2000\n",
            "Epoch 74/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.1982 - accuracy: 0.1879 - val_loss: 2.1729 - val_accuracy: 0.1990\n",
            "Epoch 75/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.1972 - accuracy: 0.1837 - val_loss: 2.1715 - val_accuracy: 0.1995\n",
            "Network takes 169.704 seconds to train\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D-kcR7X1b3CO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f806448c-c941-4f4d-8536-8fa312d05083"
      },
      "source": [
        "model = get_training_model()\n",
        "\n",
        "for layer in model.layers:\n",
        "    if not isinstance(layer, tf.keras.layers.BatchNormalization):\n",
        "        if hasattr(layer, \"trainable\"):\n",
        "            layer.trainable=False\n",
        "\n",
        "# Train model with a decay schedule\n",
        "first_decay_steps = 1000\n",
        "lr_decayed_fn = (\n",
        "  tf.keras.experimental.CosineDecayRestarts(\n",
        "      initial_learning_rate=0.1,\n",
        "      first_decay_steps=first_decay_steps))\n",
        "\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=tf.keras.optimizers.SGD(lr_decayed_fn), metrics=[\"accuracy\"])\n",
        "\n",
        "start = time.time()\n",
        "h = model.fit(train_ds,\n",
        "         validation_data=test_ds,\n",
        "         epochs=75)\n",
        "end = time.time()\n",
        "print(\"Network takes {:.3f} seconds to train\".format(end - start))\n",
        "plot_training(h)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/75\n",
            "79/79 [==============================] - 3s 32ms/step - loss: 2.3823 - accuracy: 0.1155 - val_loss: 2.2982 - val_accuracy: 0.0945\n",
            "Epoch 2/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.3319 - accuracy: 0.1205 - val_loss: 2.2864 - val_accuracy: 0.1125\n",
            "Epoch 3/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.3010 - accuracy: 0.1252 - val_loss: 2.2740 - val_accuracy: 0.1345\n",
            "Epoch 4/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2809 - accuracy: 0.1289 - val_loss: 2.2533 - val_accuracy: 0.1625\n",
            "Epoch 5/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.2595 - accuracy: 0.1487 - val_loss: 2.2380 - val_accuracy: 0.1835\n",
            "Epoch 6/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.2510 - accuracy: 0.1470 - val_loss: 2.2229 - val_accuracy: 0.1930\n",
            "Epoch 7/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2454 - accuracy: 0.1568 - val_loss: 2.2144 - val_accuracy: 0.2025\n",
            "Epoch 8/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2382 - accuracy: 0.1571 - val_loss: 2.2102 - val_accuracy: 0.2030\n",
            "Epoch 9/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.2363 - accuracy: 0.1638 - val_loss: 2.2069 - val_accuracy: 0.2060\n",
            "Epoch 10/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2323 - accuracy: 0.1653 - val_loss: 2.2053 - val_accuracy: 0.2060\n",
            "Epoch 11/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.2285 - accuracy: 0.1675 - val_loss: 2.2046 - val_accuracy: 0.2100\n",
            "Epoch 12/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.2387 - accuracy: 0.1606 - val_loss: 2.2046 - val_accuracy: 0.2110\n",
            "Epoch 13/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.2300 - accuracy: 0.1703 - val_loss: 2.2023 - val_accuracy: 0.2185\n",
            "Epoch 14/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.2192 - accuracy: 0.1744 - val_loss: 2.1891 - val_accuracy: 0.2265\n",
            "Epoch 15/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.2123 - accuracy: 0.1823 - val_loss: 2.1782 - val_accuracy: 0.2350\n",
            "Epoch 16/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.2053 - accuracy: 0.1802 - val_loss: 2.1709 - val_accuracy: 0.2425\n",
            "Epoch 17/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.1989 - accuracy: 0.1879 - val_loss: 2.1610 - val_accuracy: 0.2380\n",
            "Epoch 18/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.1770 - accuracy: 0.2004 - val_loss: 2.1524 - val_accuracy: 0.2325\n",
            "Epoch 19/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.1771 - accuracy: 0.1936 - val_loss: 2.1445 - val_accuracy: 0.2365\n",
            "Epoch 20/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.1653 - accuracy: 0.2016 - val_loss: 2.1386 - val_accuracy: 0.2315\n",
            "Epoch 21/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.1603 - accuracy: 0.1985 - val_loss: 2.1318 - val_accuracy: 0.2375\n",
            "Epoch 22/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.1517 - accuracy: 0.2056 - val_loss: 2.1287 - val_accuracy: 0.2340\n",
            "Epoch 23/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.1502 - accuracy: 0.2057 - val_loss: 2.1198 - val_accuracy: 0.2470\n",
            "Epoch 24/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.1404 - accuracy: 0.2122 - val_loss: 2.1132 - val_accuracy: 0.2440\n",
            "Epoch 25/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.1308 - accuracy: 0.2090 - val_loss: 2.1074 - val_accuracy: 0.2405\n",
            "Epoch 26/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.1338 - accuracy: 0.2086 - val_loss: 2.1022 - val_accuracy: 0.2430\n",
            "Epoch 27/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.1293 - accuracy: 0.2115 - val_loss: 2.1010 - val_accuracy: 0.2410\n",
            "Epoch 28/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.1250 - accuracy: 0.2117 - val_loss: 2.0964 - val_accuracy: 0.2435\n",
            "Epoch 29/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.1244 - accuracy: 0.2198 - val_loss: 2.0951 - val_accuracy: 0.2445\n",
            "Epoch 30/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.1214 - accuracy: 0.2147 - val_loss: 2.0925 - val_accuracy: 0.2460\n",
            "Epoch 31/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.1221 - accuracy: 0.2178 - val_loss: 2.0910 - val_accuracy: 0.2480\n",
            "Epoch 32/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.1143 - accuracy: 0.2183 - val_loss: 2.0892 - val_accuracy: 0.2505\n",
            "Epoch 33/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.1176 - accuracy: 0.2119 - val_loss: 2.0880 - val_accuracy: 0.2500\n",
            "Epoch 34/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.1126 - accuracy: 0.2206 - val_loss: 2.0882 - val_accuracy: 0.2495\n",
            "Epoch 35/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.1126 - accuracy: 0.2191 - val_loss: 2.0878 - val_accuracy: 0.2495\n",
            "Epoch 36/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.1186 - accuracy: 0.2138 - val_loss: 2.0875 - val_accuracy: 0.2490\n",
            "Epoch 37/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.1114 - accuracy: 0.2172 - val_loss: 2.0874 - val_accuracy: 0.2490\n",
            "Epoch 38/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.1141 - accuracy: 0.2158 - val_loss: 2.0878 - val_accuracy: 0.2495\n",
            "Epoch 39/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.1106 - accuracy: 0.2219 - val_loss: 2.0782 - val_accuracy: 0.2480\n",
            "Epoch 40/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.1058 - accuracy: 0.2214 - val_loss: 2.0716 - val_accuracy: 0.2510\n",
            "Epoch 41/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.0925 - accuracy: 0.2244 - val_loss: 2.0654 - val_accuracy: 0.2510\n",
            "Epoch 42/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.0921 - accuracy: 0.2223 - val_loss: 2.0601 - val_accuracy: 0.2560\n",
            "Epoch 43/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.0828 - accuracy: 0.2265 - val_loss: 2.0514 - val_accuracy: 0.2555\n",
            "Epoch 44/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.0782 - accuracy: 0.2207 - val_loss: 2.0434 - val_accuracy: 0.2635\n",
            "Epoch 45/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.0695 - accuracy: 0.2297 - val_loss: 2.0384 - val_accuracy: 0.2670\n",
            "Epoch 46/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.0687 - accuracy: 0.2325 - val_loss: 2.0351 - val_accuracy: 0.2640\n",
            "Epoch 47/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.0654 - accuracy: 0.2311 - val_loss: 2.0265 - val_accuracy: 0.2705\n",
            "Epoch 48/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.0591 - accuracy: 0.2341 - val_loss: 2.0277 - val_accuracy: 0.2690\n",
            "Epoch 49/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.0588 - accuracy: 0.2260 - val_loss: 2.0257 - val_accuracy: 0.2740\n",
            "Epoch 50/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.0537 - accuracy: 0.2365 - val_loss: 2.0173 - val_accuracy: 0.2740\n",
            "Epoch 51/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.0476 - accuracy: 0.2413 - val_loss: 2.0195 - val_accuracy: 0.2785\n",
            "Epoch 52/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.0531 - accuracy: 0.2304 - val_loss: 2.0136 - val_accuracy: 0.2740\n",
            "Epoch 53/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.0489 - accuracy: 0.2345 - val_loss: 2.0097 - val_accuracy: 0.2735\n",
            "Epoch 54/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.0372 - accuracy: 0.2346 - val_loss: 2.0069 - val_accuracy: 0.2740\n",
            "Epoch 55/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.0422 - accuracy: 0.2401 - val_loss: 2.0024 - val_accuracy: 0.2720\n",
            "Epoch 56/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.0411 - accuracy: 0.2438 - val_loss: 2.0036 - val_accuracy: 0.2735\n",
            "Epoch 57/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.0347 - accuracy: 0.2489 - val_loss: 1.9973 - val_accuracy: 0.2780\n",
            "Epoch 58/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.0332 - accuracy: 0.2434 - val_loss: 1.9970 - val_accuracy: 0.2820\n",
            "Epoch 59/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.0325 - accuracy: 0.2416 - val_loss: 1.9963 - val_accuracy: 0.2770\n",
            "Epoch 60/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.0251 - accuracy: 0.2490 - val_loss: 1.9972 - val_accuracy: 0.2775\n",
            "Epoch 61/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.0289 - accuracy: 0.2392 - val_loss: 1.9962 - val_accuracy: 0.2775\n",
            "Epoch 62/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.0348 - accuracy: 0.2419 - val_loss: 1.9925 - val_accuracy: 0.2735\n",
            "Epoch 63/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.0250 - accuracy: 0.2394 - val_loss: 1.9916 - val_accuracy: 0.2750\n",
            "Epoch 64/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.0241 - accuracy: 0.2482 - val_loss: 1.9910 - val_accuracy: 0.2790\n",
            "Epoch 65/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.0185 - accuracy: 0.2464 - val_loss: 1.9883 - val_accuracy: 0.2765\n",
            "Epoch 66/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.0243 - accuracy: 0.2428 - val_loss: 1.9855 - val_accuracy: 0.2805\n",
            "Epoch 67/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.0243 - accuracy: 0.2460 - val_loss: 1.9865 - val_accuracy: 0.2785\n",
            "Epoch 68/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.0238 - accuracy: 0.2416 - val_loss: 1.9852 - val_accuracy: 0.2780\n",
            "Epoch 69/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.0204 - accuracy: 0.2477 - val_loss: 1.9850 - val_accuracy: 0.2775\n",
            "Epoch 70/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.0126 - accuracy: 0.2578 - val_loss: 1.9840 - val_accuracy: 0.2820\n",
            "Epoch 71/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.0113 - accuracy: 0.2524 - val_loss: 1.9819 - val_accuracy: 0.2790\n",
            "Epoch 72/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.0142 - accuracy: 0.2487 - val_loss: 1.9830 - val_accuracy: 0.2760\n",
            "Epoch 73/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.0169 - accuracy: 0.2534 - val_loss: 1.9815 - val_accuracy: 0.2745\n",
            "Epoch 74/75\n",
            "79/79 [==============================] - 2s 27ms/step - loss: 2.0105 - accuracy: 0.2537 - val_loss: 1.9807 - val_accuracy: 0.2780\n",
            "Epoch 75/75\n",
            "79/79 [==============================] - 2s 28ms/step - loss: 2.0107 - accuracy: 0.2489 - val_loss: 1.9793 - val_accuracy: 0.2795\n",
            "Network takes 171.207 seconds to train\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}
